Data science is playing an increasingly important role in the financial industry. In particular, the business models of many FinTechs are based on modern machine learning and statistical learning methods. Therefore, the FAR Master’s programme focuses specifically on the diverse aspects of data science.

Special elective courses involve the in-depth explanation and computer implementation of methods and algorithms of statistical learning using practical data: 

• In the elective course “Statistical Learning in Finance and Insurance”, various algorithms and methods for supervised and unsupervised learning such as cluster analysis, support vector machines, CART, neural networks fundamentals and ensemble methods such as bagging, random forest and boosting as well as XAI techniques are introduced and implemented in R / Python.
• The seminar “Deep Learning” focuses on in-depth methods for neural networks such as Bayesian networks, feedforward networks, recurrent networks, LSTM and convolutional neural networks. These are implemented in R / Python.

Additionally, methods of statistical learning are a component of many other courses in the Master’s programme:

• Hidden Markov models for the prediction of financial market time series in the compulsory module “Stochastic Processes”
• Neural networks such as recurrent networks and LSTM for modelling time series in the compulsory module “Time Series Analysis”
• Stock market forecasts using statistical learning in the compulsory module “Stochastics of Financial Markets”
• Statistical Learning Methods for Mortality Tables in the compulsory module “Actuarial Life Insurance Methods”
• Statistical analysis of interest rates using principal component analysis and bootstrapping in the elective module “Interest Rates, Interest Rate Structure and Interest Rate Derivatives”
• Actuarial data science in non-life in the elective module “Actuarial Methods in Non-Life Insurance”
• Modelling of default probabilities with methods of statistical learning in the elective module “Credit Risk Models”

Finally, the data science focus of the FAR Master’s programme builds on extensive knowledge from the Bachelor’s degree in Business Mathematics. You can complete this knowledge within the scope of requirements as necessary: 

• in-depth knowledge of probability theory and measure theory in the modules “Probability Theory 1 and 2”
• advanced knowledge of descriptive and inductive statistics as well as modelling including multivariate regression and GLMs in the modules “Statistics 1-3” 
• Fundamentals of data science in the elective module “Statistical Learning”